A Highly Reliable Convolutional Neural Network Based Soft Tissue Sarcoma Metastasis Detection from Chest X-ray Images: A Retrospective Cohort Study

SIMPLE SUMMARY: Soft tissue sarcomas are relatively rare malignant diseases. Part of the diagnosis and follow-up includes medical imaging of the thorax for detection of lung metastases. A Python script was created and trained using a set of lung X-rays and concordant CT scans from a high-volume German-speaking sarcoma center. It is capable of detecting malignant metastasis in the lung with a precision of 71.2%, specificity of 90.5%, sensitivity of 94% and accuracy of 91.2%. Furthermore, the program was able to detect even small nodules with a size <1 cm in conventional X-rays of the thorax. This algorithm was implemented into our daily clinical practice alongside with the radiologists’ findings. With this tool we aim to improve the quality of our service and reduce the expenditure of time. ABSTRACT: Introduction: soft tissue sarcomas are a subset of malignant tumors that are relatively rare and make up 1% of all malignant tumors in adulthood. Due to the rarity of these tumors, there are significant differences in quality in the diagnosis and treatment of these tumors. One paramount aspect is the diagnosis of hematogenous metastases in the lungs. Guidelines recommend routine lung imaging by means of X-rays. With the ever advancing AI-based diagnostic support, there has so far been no implementation for sarcomas. The aim of the study was to utilize AI to obtain analyzes regarding metastasis on lung X-rays in the most possible sensitive and specific manner in sarcoma patients. Methods: a Python script was created and trained using a set of lung X-rays with sarcoma metastases from a high-volume German-speaking sarcoma center. 26 patients with lung metastasis were included. For all patients chest X-ray with corresponding lung CT scans, and histological biopsies were available. The number of trainable images were expanded to 600. In order to evaluate the biological sensitivity and specificity, the script was tested on lung X-rays with a lung CT as control. Results: in this study we present a new type of convolutional neural network-based system with a precision of 71.2%, specificity of 90.5%, sensitivity of 94%, recall of 94% and accuracy of 91.2%. A good detection of even small findings was determined. Discussion: the created script establishes the option to check lung X-rays for metastases at a safe level, especially given this rare tumor entity.